Trajectories of Risk-taking Propensity (demo)
Yun-Rui Liu
1. Overview of Project
Insert Abstract
2. Introduction of the file
The following document contains results from all analyses conducted for the manuscript titled “Trajectories of Risk-taking Propensity: A Coordinated Analysis of Longitudinal Panels”. This document is organized by different domain risk-taking propensity,including general, driving, financial, recreational, occupational, health and social domain. For each risk-taking propensity, we create 7 models (including intercept-only model, fixed effect model, linear model, linear with gender model, linear with gender interaction model, quadratic model and quadratic with gender model) and provide a table summarizing individual study model results, trajectory plots, the meta-analysis results. We tested individual predictors that are not included in the simple trajectory model in meta regression: country, continent, mean age and scale range. And the results from these models are available below. The code used to compile this file is available here (insert link)
3. Overview of panel data
3.1. The number of participants
3.2. Histogram of age distributions (all observations)
3.3. Histograms and Density Plots for every panel
SOEP
Age distribution
Risk density
HRS
Age distribution
Risk density
SAVE
Age distribution
Risk density
DNB
Age distribution
Risk density
PHF
Age distribution
Risk density
SHARE_Austria
Age distribution
Risk density
SHARE_Germany
Age distribution
Risk density
SHARE_Sweden
Age distribution
Risk density
SHARE_Netherlands
Age distribution
Risk density
SHARE_Spain
Age distribution
Risk density
SHARE_Italy
Age distribution
Risk density
SHARE_France
Age distribution
Risk density
SHARE_Denmark
Age distribution
Risk density
SHARE_Switzerland
Age distribution
Risk density
SHARE_Belgium
Age distribution
Risk density
SHARE_Israel
Age distribution
Risk density
SHARE_Czech_Republic
Age distribution
Risk density
SHARE_Slovenia
Age distribution
Risk density
SHARE_Estonia
Age distribution
Risk density
Usoc
Age distribution
Risk density
GCOE_Japan
Age distribution
Risk density
GCOE_USA
Age distribution
Risk density
HILDA
Age distribution
Risk density
LIKS
Age distribution
Risk density
4. Multi-level model results and Meta-analysis
4.1. General risk-taking
Intercept only model
Models results
Meta analysis of ICC’s results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.9707 -7.9414 -3.9414 -4.3579 0.0586
##
## tau^2 (estimated amount of total heterogeneity): 0.0155 (SE = 0.0090)
## tau (square root of estimated tau^2 value): 0.1247
## I^2 (total heterogeneity / total variability): 99.83%
## H^2 (total variability / sampling variability): 605.99
##
## Test for Heterogeneity:
## Q(df = 6) = 3997.8164, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4331 0.0473 9.1579 <.0001 0.3404 0.5259 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fixed effect model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0012 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0346
## I^2 (total heterogeneity / total variability): 98.24%
## H^2 (total variability / sampling variability): 56.78
##
## Test for Heterogeneity:
## Q(df = 6) = 251.5222, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0777 0.0135 -5.7739 <.0001 -0.1041 -0.0514 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0012 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0347
## I^2 (total heterogeneity / total variability): 98.19%
## H^2 (total variability / sampling variability): 55.19
##
## Test for Heterogeneity:
## Q(df = 6) = 231.5468, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0776 0.0135 -5.7524 <.0001 -0.1040 -0.0511 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
Linear with gender model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0013 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0365
## I^2 (total heterogeneity / total variability): 98.40%
## H^2 (total variability / sampling variability): 62.49
##
## Test for Heterogeneity:
## Q(df = 6) = 242.4521, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0785 0.0141 -5.5522 <.0001 -0.1062 -0.0508 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0120 (SE = 0.0074)
## tau (square root of estimated tau^2 value): 0.1097
## I^2 (total heterogeneity / total variability): 98.28%
## H^2 (total variability / sampling variability): 57.98
##
## Test for Heterogeneity:
## Q(df = 6) = 296.8534, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2353 0.0427 -5.5097 <.0001 -0.3190 -0.1516 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
Linear with gender interaction model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0398
## I^2 (total heterogeneity / total variability): 97.14%
## H^2 (total variability / sampling variability): 35.03
##
## Test for Heterogeneity:
## Q(df = 6) = 113.1256, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0819 0.0157 -5.2151 <.0001 -0.1127 -0.0511 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0120 (SE = 0.0074)
## tau (square root of estimated tau^2 value): 0.1096
## I^2 (total heterogeneity / total variability): 97.73%
## H^2 (total variability / sampling variability): 43.99
##
## Test for Heterogeneity:
## Q(df = 6) = 276.5847, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2268 0.0428 -5.3000 <.0001 -0.3107 -0.1429 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of age \(\times\) gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0004 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0201
## I^2 (total heterogeneity / total variability): 82.50%
## H^2 (total variability / sampling variability): 5.71
##
## Test for Heterogeneity:
## Q(df = 6) = 35.8767, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0070 0.0091 0.7720 0.4401 -0.0108 0.0248
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
Quadratic model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0004 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0211
## I^2 (total heterogeneity / total variability): 96.03%
## H^2 (total variability / sampling variability): 25.16
##
## Test for Heterogeneity:
## Q(df = 3) = 67.8368, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0836 0.0109 -7.6376 <.0001 -0.1051 -0.0621 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of age\({ }^{2}\) effect
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0001 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0122
## I^2 (total heterogeneity / total variability): 96.98%
## H^2 (total variability / sampling variability): 33.15
##
## Test for Heterogeneity:
## Q(df = 3) = 149.9145, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0016 0.0064 -0.2488 0.8035 -0.0141 0.0109
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
Quadratic with gender model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0233
## I^2 (total heterogeneity / total variability): 96.79%
## H^2 (total variability / sampling variability): 31.16
##
## Test for Heterogeneity:
## Q(df = 3) = 76.0587, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0840 0.0120 -7.0032 <.0001 -0.1075 -0.0605 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of age\({ }^{2}\) effect
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0001 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0115
## I^2 (total heterogeneity / total variability): 96.68%
## H^2 (total variability / sampling variability): 30.15
##
## Test for Heterogeneity:
## Q(df = 3) = 139.5422, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0018 0.0060 -0.2973 0.7662 -0.0136 0.0100
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of gender effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0083 (SE = 0.0070)
## tau (square root of estimated tau^2 value): 0.0909
## I^2 (total heterogeneity / total variability): 97.91%
## H^2 (total variability / sampling variability): 47.86
##
## Test for Heterogeneity:
## Q(df = 3) = 95.8378, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2858 0.0462 -6.1816 <.0001 -0.3764 -0.1952 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
4.2. Driving risk-taking
Intercept only model
Models results
Meta analysis of ICC’s results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.5503 -5.1006 -1.1006 -3.7143 10.8994
##
## tau^2 (estimated amount of total heterogeneity): 0.0045 (SE = 0.0046)
## tau (square root of estimated tau^2 value): 0.0672
## I^2 (total heterogeneity / total variability): 98.65%
## H^2 (total variability / sampling variability): 73.93
##
## Test for Heterogeneity:
## Q(df = 2) = 205.8566, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4641 0.0392 11.8491 <.0001 0.3873 0.5409 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fixed effect model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0004 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0202
## I^2 (total heterogeneity / total variability): 88.84%
## H^2 (total variability / sampling variability): 8.96
##
## Test for Heterogeneity:
## Q(df = 2) = 24.2253, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1024 0.0125 -8.1660 <.0001 -0.1270 -0.0778 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0227
## I^2 (total heterogeneity / total variability): 90.89%
## H^2 (total variability / sampling variability): 10.98
##
## Test for Heterogeneity:
## Q(df = 2) = 30.0259, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1030 0.0139 -7.4184 <.0001 -0.1303 -0.0758 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
Linear with gender model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0228
## I^2 (total heterogeneity / total variability): 91.39%
## H^2 (total variability / sampling variability): 11.61
##
## Test for Heterogeneity:
## Q(df = 2) = 31.7710, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1099 0.0139 -7.8912 <.0001 -0.1371 -0.0826 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0031 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0561
## I^2 (total heterogeneity / total variability): 89.18%
## H^2 (total variability / sampling variability): 9.24
##
## Test for Heterogeneity:
## Q(df = 2) = 16.6401, p-val = 0.0002
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3902 0.0345 -11.3253 <.0001 -0.4578 -0.3227 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
Linear with gender interaction model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0001 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0121
## I^2 (total heterogeneity / total variability): 59.11%
## H^2 (total variability / sampling variability): 2.45
##
## Test for Heterogeneity:
## Q(df = 2) = 5.0592, p-val = 0.0797
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1259 0.0092 -13.6449 <.0001 -0.1440 -0.1078 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0022 (SE = 0.0032)
## tau (square root of estimated tau^2 value): 0.0473
## I^2 (total heterogeneity / total variability): 78.07%
## H^2 (total variability / sampling variability): 4.56
##
## Test for Heterogeneity:
## Q(df = 2) = 10.2374, p-val = 0.0060
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3987 0.0322 -12.3785 <.0001 -0.4619 -0.3356 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of age \(\times\) gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0007 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0255
## I^2 (total heterogeneity / total variability): 76.61%
## H^2 (total variability / sampling variability): 4.27
##
## Test for Heterogeneity:
## Q(df = 2) = 9.0295, p-val = 0.0109
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0264 0.0172 1.5409 0.1233 -0.0072 0.0601
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
4.3. Financial risk-taking
Intercept only model
Models results
Meta analysis of ICC’s results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 14.3922 -28.7845 -24.7845 -22.8956 -24.0345
##
## tau^2 (estimated amount of total heterogeneity): 0.0122 (SE = 0.0041)
## tau (square root of estimated tau^2 value): 0.1103
## I^2 (total heterogeneity / total variability): 99.42%
## H^2 (total variability / sampling variability): 171.74
##
## Test for Heterogeneity:
## Q(df = 19) = 2251.6232, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3622 0.0253 14.3385 <.0001 0.3127 0.4117 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fixed effect model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0018 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0428
## I^2 (total heterogeneity / total variability): 97.13%
## H^2 (total variability / sampling variability): 34.88
##
## Test for Heterogeneity:
## Q(df = 19) = 448.9503, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1086 0.0101 -10.6991 <.0001 -0.1285 -0.0887 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0019 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0433
## I^2 (total heterogeneity / total variability): 96.89%
## H^2 (total variability / sampling variability): 32.15
##
## Test for Heterogeneity:
## Q(df = 19) = 411.3669, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1094 0.0102 -10.7017 <.0001 -0.1294 -0.0893 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
Linear with gender model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0017 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0417
## I^2 (total heterogeneity / total variability): 96.74%
## H^2 (total variability / sampling variability): 30.64
##
## Test for Heterogeneity:
## Q(df = 19) = 416.9017, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1107 0.0099 -11.2353 <.0001 -0.1300 -0.0914 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of gender effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0041)
## tau (square root of estimated tau^2 value): 0.1086
## I^2 (total heterogeneity / total variability): 96.60%
## H^2 (total variability / sampling variability): 29.41
##
## Test for Heterogeneity:
## Q(df = 19) = 1042.4280, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2709 0.0253 -10.7251 <.0001 -0.3204 -0.2214 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
Linear with gender interaction model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0024 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0488
## I^2 (total heterogeneity / total variability): 95.13%
## H^2 (total variability / sampling variability): 20.54
##
## Test for Heterogeneity:
## Q(df = 19) = 307.2837, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1281 0.0119 -10.7695 <.0001 -0.1514 -0.1048 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of gender effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0143 (SE = 0.0056)
## tau (square root of estimated tau^2 value): 0.1195
## I^2 (total heterogeneity / total variability): 94.95%
## H^2 (total variability / sampling variability): 19.79
##
## Test for Heterogeneity:
## Q(df = 19) = 906.4073, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3152 0.0296 -10.6553 <.0001 -0.3732 -0.2572 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of age \(\times\) gender effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0007 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0272
## I^2 (total heterogeneity / total variability): 76.73%
## H^2 (total variability / sampling variability): 4.30
##
## Test for Heterogeneity:
## Q(df = 19) = 64.7546, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0302 0.0082 3.6960 0.0002 0.0142 0.0463 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
Quadratic model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 13; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0039 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0621
## I^2 (total heterogeneity / total variability): 96.91%
## H^2 (total variability / sampling variability): 32.41
##
## Test for Heterogeneity:
## Q(df = 12) = 87.1316, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0982 0.0205 -4.7957 <.0001 -0.1383 -0.0580 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of age\({ }^{2}\) effect
##
## Random-Effects Model (k = 13; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0161
## I^2 (total heterogeneity / total variability): 91.90%
## H^2 (total variability / sampling variability): 12.35
##
## Test for Heterogeneity:
## Q(df = 12) = 277.4395, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0000 0.0053 0.0089 0.9929 -0.0104 0.0105
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
Quadratic with gender model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 13; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0041 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0639
## I^2 (total heterogeneity / total variability): 97.19%
## H^2 (total variability / sampling variability): 35.63
##
## Test for Heterogeneity:
## Q(df = 12) = 125.4651, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1183 0.0209 -5.6674 <.0001 -0.1592 -0.0774 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of age\({ }^{2}\) effect
##
## Random-Effects Model (k = 13; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0150
## I^2 (total heterogeneity / total variability): 91.05%
## H^2 (total variability / sampling variability): 11.17
##
## Test for Heterogeneity:
## Q(df = 12) = 297.3119, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0047 0.0051 0.9246 0.3552 -0.0052 0.0146
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of gender effect results
##
## Random-Effects Model (k = 13; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0117 (SE = 0.0051)
## tau (square root of estimated tau^2 value): 0.1083
## I^2 (total heterogeneity / total variability): 96.36%
## H^2 (total variability / sampling variability): 27.48
##
## Test for Heterogeneity:
## Q(df = 12) = 653.3721, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2679 0.0312 -8.5972 <.0001 -0.3289 -0.2068 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
4.4. Recreational risk-taking
Intercept only model
Models results
Meta analysis of ICC’s results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.2173 -6.4347 -2.4347 -5.0484 9.5653
##
## tau^2 (estimated amount of total heterogeneity): 0.0023 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0.0481
## I^2 (total heterogeneity / total variability): 97.89%
## H^2 (total variability / sampling variability): 47.32
##
## Test for Heterogeneity:
## Q(df = 2) = 132.5530, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4705 0.0281 16.7363 <.0001 0.4154 0.5256 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fixed effect model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0390
## I^2 (total heterogeneity / total variability): 96.70%
## H^2 (total variability / sampling variability): 30.32
##
## Test for Heterogeneity:
## Q(df = 2) = 87.7706, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1673 0.0231 -7.2544 <.0001 -0.2126 -0.1221 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0017 (SE = 0.0018)
## tau (square root of estimated tau^2 value): 0.0410
## I^2 (total heterogeneity / total variability): 97.03%
## H^2 (total variability / sampling variability): 33.69
##
## Test for Heterogeneity:
## Q(df = 2) = 100.3378, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1651 0.0242 -6.8287 <.0001 -0.2125 -0.1177 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
Linear with gender model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0015)
## tau (square root of estimated tau^2 value): 0.0382
## I^2 (total heterogeneity / total variability): 96.72%
## H^2 (total variability / sampling variability): 30.47
##
## Test for Heterogeneity:
## Q(df = 2) = 91.1460, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1701 0.0226 -7.5360 <.0001 -0.2143 -0.1258 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0036 (SE = 0.0041)
## tau (square root of estimated tau^2 value): 0.0604
## I^2 (total heterogeneity / total variability): 90.05%
## H^2 (total variability / sampling variability): 10.05
##
## Test for Heterogeneity:
## Q(df = 2) = 14.7678, p-val = 0.0006
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3696 0.0369 -10.0119 <.0001 -0.4419 -0.2972 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
Linear with gender interaction model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0020 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0451
## I^2 (total heterogeneity / total variability): 95.16%
## H^2 (total variability / sampling variability): 20.65
##
## Test for Heterogeneity:
## Q(df = 2) = 49.1470, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1809 0.0271 -6.6739 <.0001 -0.2340 -0.1278 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0000 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0036
## I^2 (total heterogeneity / total variability): 1.96%
## H^2 (total variability / sampling variability): 1.02
##
## Test for Heterogeneity:
## Q(df = 2) = 1.7319, p-val = 0.4207
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3581 0.0113 -31.6635 <.0001 -0.3803 -0.3359 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of age \(\times\) gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0009 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0301
## I^2 (total heterogeneity / total variability): 81.88%
## H^2 (total variability / sampling variability): 5.52
##
## Test for Heterogeneity:
## Q(df = 2) = 6.7812, p-val = 0.0337
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0260 0.0197 1.3230 0.1858 -0.0125 0.0646
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
4.5. Occupational risk-taking
Intercept only model
Models results
Meta analysis of ICC’s results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.7833 -5.5666 -1.5666 -4.1803 10.4334
##
## tau^2 (estimated amount of total heterogeneity): 0.0035 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0595
## I^2 (total heterogeneity / total variability): 97.97%
## H^2 (total variability / sampling variability): 49.31
##
## Test for Heterogeneity:
## Q(df = 2) = 106.4098, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4102 0.0348 11.7962 <.0001 0.3420 0.4783 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fixed effect model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0185
## I^2 (total heterogeneity / total variability): 85.54%
## H^2 (total variability / sampling variability): 6.92
##
## Test for Heterogeneity:
## Q(df = 2) = 18.3813, p-val = 0.0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1729 0.0118 -14.6125 <.0001 -0.1961 -0.1497 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0174
## I^2 (total heterogeneity / total variability): 83.99%
## H^2 (total variability / sampling variability): 6.25
##
## Test for Heterogeneity:
## Q(df = 2) = 16.3125, p-val = 0.0003
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1728 0.0112 -15.3703 <.0001 -0.1948 -0.1508 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
Linear with gender model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0159
## I^2 (total heterogeneity / total variability): 81.61%
## H^2 (total variability / sampling variability): 5.44
##
## Test for Heterogeneity:
## Q(df = 2) = 13.8301, p-val = 0.0010
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1771 0.0104 -17.0764 <.0001 -0.1975 -0.1568 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0037 (SE = 0.0042)
## tau (square root of estimated tau^2 value): 0.0606
## I^2 (total heterogeneity / total variability): 89.57%
## H^2 (total variability / sampling variability): 9.58
##
## Test for Heterogeneity:
## Q(df = 2) = 16.7250, p-val = 0.0002
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2738 0.0372 -7.3631 <.0001 -0.3466 -0.2009 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
Linear with gender interaction model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0001 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0096
## I^2 (total heterogeneity / total variability): 44.44%
## H^2 (total variability / sampling variability): 1.80
##
## Test for Heterogeneity:
## Q(df = 2) = 4.0755, p-val = 0.1303
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1772 0.0083 -21.4704 <.0001 -0.1934 -0.1610 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0034 (SE = 0.0044)
## tau (square root of estimated tau^2 value): 0.0579
## I^2 (total heterogeneity / total variability): 84.31%
## H^2 (total variability / sampling variability): 6.37
##
## Test for Heterogeneity:
## Q(df = 2) = 16.5675, p-val = 0.0003
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2456 0.0382 -6.4262 <.0001 -0.3205 -0.1707 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of age \(\times\) gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0018)
## tau (square root of estimated tau^2 value): 0.0383
## I^2 (total heterogeneity / total variability): 86.46%
## H^2 (total variability / sampling variability): 7.38
##
## Test for Heterogeneity:
## Q(df = 2) = 11.3194, p-val = 0.0035
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0074 0.0243 -0.3048 0.7606 -0.0551 0.0403
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
4.6. Health risk-taking
Intercept only model
Models results
Meta analysis of ICC’s results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.2235 -6.4470 -2.4470 -5.0607 9.5530
##
## tau^2 (estimated amount of total heterogeneity): 0.0022 (SE = 0.0023)
## tau (square root of estimated tau^2 value): 0.0474
## I^2 (total heterogeneity / total variability): 97.11%
## H^2 (total variability / sampling variability): 34.62
##
## Test for Heterogeneity:
## Q(df = 2) = 72.5854, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3801 0.0279 13.6268 <.0001 0.3255 0.4348 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fixed effect model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0396
## I^2 (total heterogeneity / total variability): 97.21%
## H^2 (total variability / sampling variability): 35.81
##
## Test for Heterogeneity:
## Q(df = 2) = 92.3561, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0867 0.0234 -3.7114 0.0002 -0.1325 -0.0409 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0017)
## tau (square root of estimated tau^2 value): 0.0400
## I^2 (total heterogeneity / total variability): 97.27%
## H^2 (total variability / sampling variability): 36.68
##
## Test for Heterogeneity:
## Q(df = 2) = 95.8506, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0871 0.0236 -3.6934 0.0002 -0.1333 -0.0409 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
Linear with gender model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0392
## I^2 (total heterogeneity / total variability): 97.22%
## H^2 (total variability / sampling variability): 35.93
##
## Test for Heterogeneity:
## Q(df = 2) = 91.0157, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0891 0.0231 -3.8566 0.0001 -0.1344 -0.0438 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0019 (SE = 0.0023)
## tau (square root of estimated tau^2 value): 0.0432
## I^2 (total heterogeneity / total variability): 83.54%
## H^2 (total variability / sampling variability): 6.08
##
## Test for Heterogeneity:
## Q(df = 2) = 13.8841, p-val = 0.0010
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2473 0.0275 -9.0104 <.0001 -0.3012 -0.1935 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
Linear with gender interaction model
Models results
Meta analysis of age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0013 (SE = 0.0014)
## tau (square root of estimated tau^2 value): 0.0359
## I^2 (total heterogeneity / total variability): 93.31%
## H^2 (total variability / sampling variability): 14.95
##
## Test for Heterogeneity:
## Q(df = 2) = 44.9010, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0953 0.0219 -4.3459 <.0001 -0.1383 -0.0523 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0029 (SE = 0.0039)
## tau (square root of estimated tau^2 value): 0.0539
## I^2 (total heterogeneity / total variability): 83.92%
## H^2 (total variability / sampling variability): 6.22
##
## Test for Heterogeneity:
## Q(df = 2) = 14.2671, p-val = 0.0008
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2395 0.0356 -6.7208 <.0001 -0.3094 -0.1697 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis of age \(\times\) gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.0008 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0286
## I^2 (total heterogeneity / total variability): 82.08%
## H^2 (total variability / sampling variability): 5.58
##
## Test for Heterogeneity:
## Q(df = 2) = 6.6315, p-val = 0.0363
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0183 0.0187 0.9788 0.3277 -0.0184 0.0551
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot
5. Meta analytic summary of slope estimates
Intercept-only model
Fixed effect model
Linear model
Linear with gender model
Linear with gender interaction model
Quadratic model
Quadratic with gender model
4.7. Social risk-taking